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Research Papers

Statistic Determination of Storage Capacity for Photovoltaic Energy Imbalance Mitigation

[+] Author and Article Information
Corrado Giammanco

Department of Electronic Engineering,
University of Rome Tor Vergata,
Via del Politecnico, 1,
Rome 00133, Italy
e-mail: corado.giammanco@uniroma2.it

Marco Pierro, Cristina Cornaro

Department of Enterprise Engineering,
University of Rome Tor Vergata,
Via del Politecnico, 1,
Rome 00133, Italy

Aldo di Carlo

Department of Electronic Engineering,
University of Rome Tor Vergata,
Via del Politecnico, 1,
Rome 00133, Italy

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received March 20, 2015; final manuscript received September 2, 2015; published online October 29, 2015. Assoc. Editor: M. Keith Sharp.

J. Sol. Energy Eng 138(1), 011002 (Oct 29, 2015) (5 pages) Paper No: SOL-15-1073; doi: 10.1115/1.4031801 History: Received March 20, 2015; Revised September 02, 2015

This paper describes a methodology to evaluate the storage capacity that should support a photo-voltaic (PV) power plant in order to reduce the power imbalance generated by the forecast error. This is obtained through a probabilistic analysis performed on an ensemble of synthetic data. The synthetic data signals are generated starting from at least 1 year of measured data and reproduce the same statistical distribution and the same Fourier power spectrum (FPS) shape.

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References

Council of the European Union, 2007, Brussels European Council, Mar. 8/9, 2007. Presidency Conclusions, May 2, 2007, Brussels, Belgium.
Klessmann, C. , Held, A. , Rathmann, M. , and Ragwitz, M. , 2011, “ Status and Perspectives of Renewable Energy Policy and Deployment in the European Union–What Is Needed to Reach the 2020 Targets?” Energy Policy, 39(12), pp. 7637–7657. [CrossRef]
Möst, D. , and Fichtner, W. , 2010, “ Renewable Energy Sources in European Energy Supply and Interactions With Emission Trading,” Energy Policy, 38(6), pp. 2898–2910. [CrossRef]
Tuohy, A. , Meibom, P. , Denny, E. , and O'Malley, M. , 2009, “ Unit Commitment for Systems With Significant Wind Penetration,” IEEE Trans. Power Syst., 24(2), pp. 592–601. [CrossRef]
Chattopadhyay, D. , 2011, “ Scale Efficient Network Development to Support Renewable Generation Development,” IEEE Trans. Sustainable Energy, 2(3), pp. 329–339. [CrossRef]
Evans, A. , Strezov, V. , and Evans, T. J. , 2012, “ Assessment of Utility Energy Storage Options for Increased Renewable Energy Penetration,” Renewable Sustainable Energy Rev., 16(6), pp. 4141–4147. [CrossRef]
Teleke, S. , Baran, M. E. , Huang, A. Q. , Bhattacharya, S. , and Anderson, L. , 2009, “ Control Strategies for Battery Energy Storage for Wind Farm Dispatching,” IEEE Trans. Energy Convers., 24(3), pp. 725–732. [CrossRef]
Swierczynski, M. , Stroe, D. I. , Stan, A. I. , and Teodorescu, R. , 2015, “ Lifetime and Economic Analyses of Lithium-Ion Batteries for Balancing Wind Power Forecast Error,” Int. J. Energy Res., 39(6), pp. 760–770. [CrossRef]
Madsen, H. , Pinson, P. , Kariniotakis, G. , Haa, N. , and Nielsen, T. S. , 2005, “ Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models,” Wind Eng., 29(6), pp. 475–489. [CrossRef]
Bludszuweit, H. , Domínguez-Navarro, J. A. , and Llombart, A. , 2008, “ Statistical Analysis of Wind Power Forecast Error,” IEEE Trans. Power Syst., 23(3), pp. 983–991. [CrossRef]
Rudolf, V. , and Papastergiou, K. D. , 2013, “ Financial Analysis of Utility Scale Photovoltaic Plants With Battery Energy Storage,” Energy Policy, 63, pp. 139–146. [CrossRef]
Hanna, R. , Kleissl, J. , Nottrott, A. , and Ferry, M. , 2014, “ Energy Dispatch Schedule Optimization for Demand Charge Reduction Using a Photovoltaic-Battery Storage System With Solar Forecasting,” Sol. Energy, 103, pp. 269–287. [CrossRef]
Kabir, M. N. , Mishra, Y. , Ledwich, G. , Xu, Z. , and Bansal, R. C. , 2014, “ Improving Voltage Profile of Residential Distribution Systems Using Rooftop PVs and Battery Energy Storage Systems,” Appl. Energy, 134, pp. 290–300. [CrossRef]
Pierro, M. , Bucci, F. , Cornaro, C. , Maggioni, E. , Perotto, A. , Pravettoni, M. , and Spada, F. , 2015, “ Model Output Statistics Cascade to Improve Day Ahead Solar Irradiance Forecast,” Sol. Energy, 117, pp. 99–113. [CrossRef]
Cornaro, C. , Pierro, M. , and Bucci, F. , 2014, “ Master Optimization Process Based on Neural Networks Ensemble for 24-h Solar Irradiance Forecast,” Sol. Energy, 111, pp. 297–312. [CrossRef]
Eisencraft, M. , and Kato, D. M. , 2009, “ Spectral Properties of Chaotic Signals With Applications in Communications,” Nonlinear Anal., 71(12), pp. e2592–e2599. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

The figures show the forecast error probability density for the four seasonal behavior: autumn (a), winter (b), spring (c), and summer (d). The continuous lines show the best fits obtained with the Gauss–Lorentz function.

Grahic Jump Location
Fig. 3

In the top panel (a), the black curve represents the probability density for the real signal and the overlapped red curve (for color version, grey curve for black and white version) represents the values obtained for a synthetic one. In the bottom panel (b), the related Fourier power spectra are superimposed. The real signal is again in black and the synthetic one is in red.

Grahic Jump Location
Fig. 2

Module of the fast-Fourier transform of the autumnal error signal. The abscissa follows the usual convection of FFT routines.

Grahic Jump Location
Fig. 6

Fraction of MAE reduction with probability 1, as a function of the installed capacity. The three lines are obtained assuming: first, that every year the forecast ability will be the same obtained by the measured data; second, that the weather stability determines an improvement of 10%; and finally, that there is a worsening of 10%.

Grahic Jump Location
Fig. 5

Probability to reduce the MAE to the fraction reported in abscissa, given an installed capacity of 0.1–3 Wh/Wp

Grahic Jump Location
Fig. 4

Probability of MAE reduction for a fraction of 50%, 40%, or 30%, as a function of the installed capacity assuming a storage efficiency of 0.8

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